Popularity prediction models have been established using the Hawkes process [1]}, linear regression [2]}, deep reinforcement learning [3]}, or entity linking and LSTMs [4]}. Generally speaking, the methodology of these studies watches the first several hours of a thread (or Twitter cascade) and then constructs a model to predict the remainder of the thread. The first several hours are critical because early attention is strongly correlated to high popularity [5]}. Instead, we ask a more difficult question: Can final cascade size and shape be predicted when none or only a few comments are available?
